Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach
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Date
2019-03-18
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Citation of Original Publication
Antonio Di Noia, et.al, Neural network cloud retrievals from POLDER, AMT , Articles, Volume 12, issue 3, Atmos. Meas. Tech., 12, 1697-1716, 2019 https://doi.org/10.5194/amt-12-1697-2019
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
Abstract
This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth’s
Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences
coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle,
multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of
POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer
(MODIS) products show that the neural network algorithm
has a low bias of around 2 in cloud optical thickness (COT)
and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the
proposed scheme may have enhanced capabilities for cloud
effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and
effective radius retrievals at the native POLDER-3 Level 1B
pixel level.